Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
An introduction to variable and feature selection
The Journal of Machine Learning Research
Mlps (mono layer polynomials and multi layer perceptrons) for nonlinear modeling
The Journal of Machine Learning Research
Ranking a random feature for variable and feature selection
The Journal of Machine Learning Research
Maximin Latin Hypercube Designs in Two Dimensions
Operations Research
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When analysing data from computationally expensive simulation codes or process measurements, surrogate modelling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualisation and optimisation. Kriging is a popular surrogate modelling technique for data based on deterministic computer experiments. There exist several types of Kriging, mostly differing in the type of regression function used. Recently a promising new variable selection technique was proposed to identify a regression function in the Kriging framework. In this paper this type of Kriging, i.e., blind Kriging, has been efficiently implemented in Matlab(R) and has been extended. The implementation is validated and tested on several examples to illustrate the strength and weaknesses of this new, promising modelling technique. It is shown that the performance of blind Kriging is as good as, or better than ordinary Kriging. Though, blind Kriging comes at double the computational cost with respect to ordinary Kriging.